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Proceedings Paper

Software-based diffusion MR human brain phantom for evaluating fiber-tracking algorithms
Author(s): Yundi Shi; Gwendoline Roger; Clement Vachet; Francois Budin; Eric Maltbie; Audrey Verde; Marion Hoogstoel; Jean-Baptiste Berger; Martin Styner
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Paper Abstract

Fiber tracking provides insights into the brain white matter network and has become more and more popular in diffusion magnetic resonance (MR) imaging. Hardware or software phantom provides an essential platform to investigate, validate and compare various tractography algorithms towards a "gold standard". Software phantoms excel due to their flexibility in varying imaging parameters, such as tissue composition, SNR, as well as potential to model various anatomies and pathologies. This paper describes a novel method in generating diffusion MR images with various imaging parameters from realistically appearing, individually varying brain anatomy based on predefined fiber tracts within a high-resolution human brain atlas. Specifically, joint, high resolution DWI and structural MRI brain atlases were constructed with images acquired from 6 healthy subjects (age 22-26) for the DWI data and 56 healthy subject (age 18-59) for the structural MRI data. Full brain fiber tracking was performed with filtered, two-tensor tractography in atlas space. A deformation field based principal component model from the structural MRI as well as unbiased atlas building was then employed to generate synthetic structural brain MR images that are individually varying. Atlas fiber tracts were accordingly warped into each synthetic brain anatomy. Diffusion MR images were finally computed from these warped tracts via a composite hindered and restricted model of diffusion with various imaging parameters for gradient directions, image resolution and SNR. Furthermore, an open-source program was developed to evaluate the fiber tracking results both qualitatively and quantitatively based on various similarity measures.

Paper Details

Date Published: 13 March 2013
PDF: 6 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86692A (13 March 2013); doi: 10.1117/12.2006113
Show Author Affiliations
Yundi Shi, Univ. of North Carolina at Chapel Hill (United States)
Gwendoline Roger, Univ. of North Carolina at Chapel Hill (United States)
Clement Vachet, Univ. of North Carolina at Chapel Hill (United States)
Francois Budin, Univ. of North Carolina at Chapel Hill (United States)
Eric Maltbie, Univ. of North Carolina at Chapel Hill (United States)
Audrey Verde, Univ. of North Carolina at Chapel Hill (United States)
Marion Hoogstoel, Univ. of North Carolina at Chapel Hill (United States)
Jean-Baptiste Berger, Univ. of North Carolina at Chapel Hill (United States)
Martin Styner, Univ. of North Carolina at Chapel Hill (United States)


Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)

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